Data Engineering & Analytics Infrastructure Topics
Data pipeline design, ETL/ELT processes, streaming architectures, data warehousing infrastructure, analytics platform design, and real-time data processing. Covers event-driven systems, batch and streaming trade-offs, data quality and governance at scale, schema design for analytics, and infrastructure for big data processing. Distinct from Data Science & Analytics (which focuses on statistical analysis and insights) and from Cloud & Infrastructure (platform-focused rather than data-flow focused).
Experimentation Platforms and Infrastructure
Infrastructure for A/B testing and experimentation: assignment/bucketing, metric pipelines for experiments, guardrail and variance-reduction plumbing, and experiment result storage. Covers building the platform that powers trustworthy online experiments at scale. Distinct from the statistics of experiment analysis.
Data Reliability and Fault Tolerance
Designing pipelines that survive failures: retries, idempotency, checkpointing, exactly-once semantics, dead-letter handling, and recovery/replay. Covers reasoning about partial failures, poison messages, and consistency guarantees under faults. The resilience angle distinct from monitoring (detecting and alerting on a failure) and from Workflow Orchestration and Scheduling (the DAG/scheduler mechanics that decide whether and when a task runs again, including backfills and dependency management): this topic owns whether the data itself stays correct, not lost, not duplicated, not corrupted, when a process is retried or replayed.
Data Ingestion and Source System Integration
Getting data out of heterogeneous source systems and landing it reliably: APIs, operational databases, file drops, webhooks, message queues and third-party SaaS. Covers connector selection and design (managed platforms versus Debezium, DMS or Kafka Connect versus building your own), pull versus push and polling versus webhook patterns, incremental extraction and high-watermark strategy including what to do when a source offers no native change capture, authentication and credential rotation against third-party APIs, source-side rate limits and quotas, schema drift and contract breakage at the source boundary, backfill and replay of history, ingestion-time data-quality gates, reconciliation after a source outage, and negotiating with source-system owners. The scope stops at the boundary: once data has landed, transforming it, the architecture of the pipeline that carries it, stream-processing mechanics, and pipeline monitoring are all covered separately.
Data Governance, Contracts, and Classification
Governing data at scale: data contracts between producers and consumers, schema evolution/compatibility, data classification and sensitivity tagging, access control, and lineage/cataloging. Covers policy, ownership, and compliance-driven controls over data. The governance layer over the technical stack.
Stream Processing and Event Streaming
Building on event-streaming platforms: Kafka and message queues, event sourcing, partitioning, consumer groups, exactly-once vs at-least-once delivery, and windowing. Covers handling late and out-of-order events, watermarks, and stateful stream operators. The core skill for real-time data engineering.
Batch, Streaming, and Real-Time Serving Trade-offs
Reasoning about when to use batch, micro-batch, or continuous streaming and how to serve low-latency analytics: latency, cost, complexity, and correctness trade-offs; lambda vs kappa architectures; and reprocessing semantics. Covers real-time aggregation, freshness vs consistency trade-offs, and reconciling streaming results with batch ground truth, including geospatial and high-throughput real-time workloads under eventual consistency. The data-systems judgment topic for choosing and reconciling batch versus real-time approaches, distinct from the hands-on streaming transport itself.
Product Analytics Instrumentation and Event Tracking
Instrumenting products to collect behavioral data: event taxonomy/tracking plans, client and server-side collection, attribution implementation, and telemetry for web, mobile, and games (including crash reporting). Covers designing clean, analyzable event schemas and the collection infrastructure behind them. The data-collection foundation for product analytics.